THEME OF 2026: AI & LIFE SCIENCE: WHAT’S NEXT?
Artificial intelligence is redefining what is possible in Life Science. The theme “AI & Life Science: What’s Next?” explores the latest breakthroughs, key challenges, and future opportunities in AI-driven healthcare and biotechnology. As this year’s theme, it highlights how rapidly evolving AI technologies are transforming every stage of the life science ecosystem. From research and diagnostics to drug development and patient care, this virtual conference brings together Swedish and American perspectives to discuss how innovation is shaping the next era of Life Science.
WHAT IS LIFE SCIENCE BRIDGE?
The Life Science Bridge is a joint initiative between the regional Swedish-American Chambers of Commerce in San Diego, San Francisco/Silicon Valley, New England, and Minnesota. The initiative supports life science companies by offering market insights, networking opportunities, and events that foster connections, collaboration, and knowledge exchange between Sweden and the leading U.S. life science ecosystems.
We invite you to take part in this year’s Bridging the Gap and engage in a forward looking dialogue at the intersection of artificial intelligence and life science. Join peers, researchers, and industry representatives from Sweden and the United States to exchange perspectives, build meaningful connections, and contribute to shaping the next phase of innovation in the life science sector.
Introduction of the speakers
Dr. Krishna Allamneni is the Chief Development Officer & Executive Vice President at Concarlo Therapeutics and Senior Advisor at Ventures Accelerated AB.
With 25+ years of experience in biopharmaceutical leadership, Dr. Allamneni specializes in early drug development and corporate strategy, guiding therapeutics from discovery to commercialization across multiple therapeutic areas and modalities.
Dr. Erik Hemberg is a research scientist at MIT CSAIL specializing in artificial intelligence and data-driven methods across life sciences, cybersecurity, and complex systems. With over 15 years of experience in AI, machine learning, and data science, his work bridges cutting-edge computational techniques with real-world applications in neuroscience, learning systems, and biomedical and security domains.
He has collaborated with leading organizations including OpenAI, MIT-IBM AI Alliance, DARPA, MIT Lincoln Laboratory, and U.S. government agencies, securing competitive research funding for interdisciplinary AI initiatives.
Marek Szczygiel leads Anyo Labs, combining his medical background with a strong focus on technology and innovation. Educated at Poznan University of Medical Sciences and Chalmers University of Technology, he combines clinical expertise with entrepreneurial strategy.
At Anyo Labs, he uses AI and advanced computational modeling to help pharma and biotech companies identify and optimize promising drug candidates earlier in development, accelerating timelines and reducing reliance on traditional lab work.
Wing Cheng is Senior Business Developer in Life Science at Uppsala Innovation Centre (UIC) and board professional in life science start-ups.
Wing works closely with emerging companies at UIC and serves as chairman of LanteRNA, while also mentoring entrepreneurs through the Nordic Mentor Network for Entrepreneurship.
His background includes leadership roles at Immuneed, Biovica, Thermo Fisher Scientific, and Cepheid, as well as regulatory work at the Swedish Medical Products Agency and TLV.
Martin Kjellberg is a PhD Student in Genetics at Stanford University. His research sits at the intersection of AI and genomics, using machine learning and multi-omics data to advance precision medicine.
With a background in Medical Biotechnology from KTH, Martin develops deep learning methods to analyze whole-genome sequencing and uncover genetic drivers of complex diseases.
Christopher Tignanelli is an Associate Professor of Surgery and Associate Dean for Data Science at the University of Minnesota. An acute care surgeon specializing in trauma, emergency surgery, and critical care, he is triple board-certified in General Surgery, Surgical Critical Care, and Clinical Informatics.
Dr. Tignanelli leads the MNCCORE research lab, a multidisciplinary team of more than 30 researchers focused on improving healthcare delivery through data science. His research centers on developing and implementing machine learning and AI-enabled clinical decision support systems that enhance evidence-based care.
In addition to his clinical and academic roles, he co-directs the University of Minnesota’s Program for Clinical Artificial Intelligence and the Quality Outcomes, Discovery and Evaluation Core, which oversees the university’s clinical data repository.
